Testing SCP hypothesis amid growing consolidation in the Indian banking sector

Testing SCP hypothesis amid growing consolidation in the Indian banking sector | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Testing SCP hypothesis amid growing consolidation in the Indian banking sector Dilawar Ahmad Bhat, Himanshu Seth, Shahida Rasheed, Irshad Ahmad Malik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5406984/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2025 Read the published version in Journal of Economic Structures → Version 1 posted 5 You are reading this latest preprint version Abstract This paper investigates market structure, conduct, and performance in the context of the banking industry of India, and, in particular, the effects of consolidation. Using an unbalanced panel dataset comprising 30 banks from 2010 to 2020, the effects of changes in the degree of market concentration, interest rate spreads, and profitability are explored through panel Vector Auto-regression (PVAR) method. By employing Concentration Ratio (CR4) as a measure for consolidation, we analyse how the changes in structure of the sector affect the conduct and performance. Our findings indicate that banks operating in more concentrated markets tend to enjoy sustained profitability in the short term, as reflected by the positive relationship between market concentration (CR4) and interest rate spreads (IRS). However, competition continues to moderate this relationship, and the effects of concentration are not fully captured by market-based performance measures like Tobin's Q. This suggests that market participants anticipate future competition or regulatory intervention, which could mitigate the benefits of concentration over time. The findings provide important implications for the regulatory policy and managerial strategies of the banking system in view of the ongoing banking consolidation processes. JEL Codes: G21, L11, C33, L13, E44 Banking consolidation Market concentration Structure-Conduct-Performance (SCP) Panel VAR analysis Indian banking sector Introduction Since the economic reforms of 1991, the Indian banking sector has been on a path of development, growth, and reform. The remarkable reform measures undertaken include major easing of financial repression; doing away with interest rate controls, entry of private sector banks, more functional and operational autonomy to public financial institutions, laying the roadmap for financial integration and globalization, and bolstering the safety mechanisms for stability of the financial system by adopting capital adequacy norms (Mohan, 2006). The arrival of new private banks and the proliferation of Non-Banking Financial Companies (NBFCs) have disrupted the traditional contours of competition, leading to a radical reconfiguration of the market's structure. Simultaneously, a wave of consolidations has swept through the sector, merging entities into larger, more formidable institutions[1], thus concentrating market power and altering the competitive dynamics that once prevailed. These structural shifts have been mirrored by significant changes in market conduct. Over the past two decades, there has been massive consolidation in the Indian banking sector hence leading to a better banking system. The process started with the amalgamation of ICICI Bank and ICICI Limited in the year 2002 and Bank of Punjab merged with Centurion Bank during the year 2004. The decade of 2010 also witnessed consolidation where State Bank of Indore merged with the SBI and Kotak Mahindra Bank acquired ING Vysya Bank in 2015. A significant increase in mergers started since 2017 when SBI merged with five of its associates as well as Bharatiya Mahila Bank; PNB with Oriental Bank of Commerce and United Bank of India, Canara Bank with Syndicate Bank; Union Bank of India amalgamating with Andhra Bank and Corporation Bank; Allahabad Bank merged with Indian Bank. These mergers have shrunk the number of public sector banks from 27 as of the end of year 2017 to 12 by the end of year 2020. Overall, the number of banks has gradually declined, be they private or public ones, which have resulted in the creation of fewer but better banks able to foster the economy’s growth and compete at the international level. Digital banking innovation has altered the existing banking paradigms, as the use of fintech innovations particularly UPI provides a new dimension of customer relations. In addition, with the divestment of Development Financial Institutions (DFIs) and the adoption of Insolvency and Bankruptcy Code (IBC) there are new constraints on banking practices – particularly risk mitigation and NPA management. The IBC has specifically introduced a new discipline in the sector forcing the banks to transform its lending mechanisms by mitigating forbearance lending All these changes have implications for the performance of the banking sector in the country. The intense competition from private banks and NBFCs along with new technology have compelled the banks to be more productive but simultaneously it posed new threats to the profit margins and market shares. Also, the mergers have led to the emergence of stronger banks that can operate under changing economic systems. Thus, the Structure-Conduct-Performance (S-C-P) paradigm appears to be particularly useful in such a dynamic context. This paper uses a panel VAR to examine the changes in market conduct brought by the structural transformation of the banking industry and its impact on performance consequences. In examining these dynamics, this study aims to shed light on the continued transformation of the Indian banking system and its implications for regulations and future strategies. Background Theory Previous research into the dynamics between market structure and performance revolves around two main theories namely the Structure-Conduct-Performance (SCP) hypothesis and the Efficient Structure Hypothesis (ESH). According to the SCP hypothesis, a rise in the market concentration in the banking industry results into an increase in the market power which, in turn, enhances pricing and profitability due to collusion opportunities. While the EHS hypothesis posits that This process works in reverse, that is, cutting-edge technology or superior management practices drive firms to become more profitable and thus more concentrated The SCP framework based on the works of Mason ( 1939 ) and Bain ( 1951 ) postulates that the conduct and performance are the internal factors while the market structure is determined by external factors. This theory suggests that concentrated banking industry increases the market power, where banks can gain monopolistic profits by offering lower deposit rates and charging higher loan rates. Market structure here is usually measured with the help of indicators like concentration ratio or the Hirschman-Herfindahl Index. On the other hand, the ESH, as introduced by Demsetz ( 1973 ) posits that market concentration emerges in a reverse fashion whereby efficient firms tend to secure bigger market shares. Companies that perform well by having superior technology or managers are more productive and hence earn more profits which translate to a larger market share that is a characteristic of increased concentration. Thus, according to This perspective, efficiency of banks is the primary factor influencing market concentration. Regarding the ESH, studies by Smirlock ( 1985 ), Evanoff and Fortier ( 1988 ), and Berger ( 1995 ) support the hypothesis, showing that higher profits are associated with larger market shares, without a direct causal link between concentration and profitability. Berger ( 1995 ) finds only weak evidence of a positive relationship between efficiency and market power, while Berger and Hannan ( 1989 ) indicate that efficiency negatively impacts market structure in U.S. banks. The traditional Structure-Conduct-Performance (SCP) paradigm, rooted in neoclassical theory, has faced significant critique. Critics like Davies et al. ( 1989 ) argue that SCP lacks a rigorous theoretical foundation, while Scherer and Ross ( 1990 ) contend that its deterministic nature fails to capture the complexities of imperfect markets. The most pointed criticism, however, is SCP's unidirectional causality, which later literature challenges by suggesting a two-way relationship between structure, conduct, and performance. Demsetz ( 1973 ) offers the Efficient Structure Hypothesis (ESH) as an alternative, arguing that market concentration results from superior firm efficiency rather than leading to it. Shepherd ( 1982 ) presents empirical evidence about Relative-Market-Power Hypothesis (RMP) that suggests that the firms with greater market shares and differentiated products, have greater market power and profitability. This is taken a notch higher by Schmalensee ( 1987 ) in the Hybrid Collusion-Efficiency Hypothesis where he combines both SCP and ESH to advance the notion that both concentration and efficiency affect performance. SCP is countered by Baumol et al. ( 1982 ) with the theory of market contestability which looks at the threat of competition rather than structure. However, Schumpeter ( 1942 ) along with other post-classical economists of the Austrian School of Economics, move the lens of focus to dynamic competition, claiming that competition is a process not a structure. The subject on game theory and Nash equilibrium during the period of 1980s and 1990s, as with the works of Clarke and Davies ( 1982 ) and Tirole ( 1988 ), shifted the focus from the structure to the conduct in industrial organization. However, SCP is still a valuable model, especially, where the governmental regulations are well developed and affect the structure. Thus, in the context of a heavily regulated industry like the Indian Banking Industry, the Structure Conduct Performance (SCP) paradigm is a more appropriate tool for analysis than the Efficient Structure Hypothesis (ESH). Since regulatory bodies, particularly RBI, have a massive impact on the market, the SCP framework, which explains how the structure of the market depending on the external conditions, including regulatory policies, affects the behaviour and performance of the banks, is quite suitable. Literature Review Theoretical literature suggests that consolidation shows that it has implications on the trade-off between profit, control and risk. It has been seen that the banks having higher levels of market concentration have lower levels of competition and therefore can be more profitable but at the same time the situation of monopolistic competition that prevails in such markets can lead to systemic instability unless properly managed. Some researchers are of the view that market power enables banks to undertake fewer risks and this enhances stability (Tabak et al., 2015). Nonetheless, critics argue that a decrease in competition leads to inefficiency and can hamper innovation (Boyd and De Nicolo, 2005 ). The relationship between market power and risk-taking is widely debated. Ariss ( 2010 ) and Berger et al. ( 1999 ) opine that increased concentration of banks makes them averse to risk particularly under capital regulation While Beck et al. ( 2006 ) notes that extreme concentration may cause moral hazard where institutions feel they are too big to fail they can engage in riskier activities. There is also controversy regarding the relationship between regulation and profitability: some of the researches indicate that high regulation hinders profitability while others affirm that if regulations are properly developed they help to provide stability profitable in the long run (Delis et al., 2011 ). Basel III for example was meant to strengthen the banking sector but it reduced liquidity and made operations more complicated according to Schwerter ( 2012 ). Internationally literature has documented studies that relates market concentration, competition and bank profitability. In the study conducted by O’Connell ( 2023 ) for the UK banking sector, the SCP hypothesis has been rejected; however, macro factors like interest rate and inflation were identified to have a direct influence over profitability. Similarly, in Kenya, Sahile et al. ( 2015 ) found that efficiency, rather than market concentration, was the primary driver of profitability, challenging the traditional SCP framework. VanHoose ( 2022 ) supplemented the previous discussion of market power and efficiency demonstrating that while scale expansion can lead to competitive pricing, concentrated markets can still have complex dynamics between market structure and behaviour. Mateev et al. ( 2021 ) observed that higher competition in the MENA region corresponds to the higher capital ratio and curbed risky behaviour during the COVID-19 outbreak; thus, competition plays a part in moderating performance results. Shair et al. ( 2019 ) proved that in the context of Pakistan, the liquidity risk had a positive impact on the profitability but the competition was found to be detrimental to the profitability. SCP hypothesis was supported by Saif-Alyousfi ( 2022 ) among 47 Asian countries as he confirmed that market concentration has a positive effect on the firm’s profitability during financial crisis. SCP hypothesis was rejected by Alhassan et al ( 2015 ) who established that technical efficiency was the primary determinant of profitability in the context of Ghanaian firms. In the Indian context, previous studies about the relationship between the market structure and performance, have yielded inconclusive results. The studies of Sinha and Sharma ( 2015 ) provides evidence of moderate level of profit persistence in the Indian banks in support of the SCP hypothesis and capital adequacy and operating efficiency have emerged as key drivers of profitability. Similarly, Mishra and Sahoo ( 2012 ) have endorsed SCP hypothesis and examined the influential relationships between market structure, banking conduct and performance. Contrary to the SCP hypothesis, Barua, Roy, and Raychaudhuri ( 2016 ) found a negative relationship between market concentration and profitability. They found capitalization, credit risk and leverage as the primary determinants of bank profitability. Ansari and Goyal ( 2014 ) examined the relationship interest rate spreads and market concentration, finding that increased competition lowers loan rates and spreads, but managerial inefficiency and regulatory constraints can exacerbate spreads. Regulatory frameworks, such as those implemented by the RBI, have aimed to strengthen banks' competitiveness and resilience in the face of global turbulence (Barth et al., 2004 ). In the micro-insurance sector, Banerjee and Savitha ( 2021 ) found that firms in the Indian life microinsurance industry performed better when competitive pressures were lower, supporting the SCP hypothesis. Their findings emphasize the significance of market structure in determining profitability in niche markets. Lastly, the Indian banking industry has changed its structure in terms of consolidation and regulatory reforms within a short span of time and hence altered the competitive landscape comprehensively. This has been evidenced by improved Concentration ratios and the Herfindahl-Hirschman Index (HHI) ratios over time driven by the RBI’s intention to purge the industry of weaker players. While consolidation improves the balance sheet resilience and operating performance, it can hamper competition and innovation (Buch & Dages, 2018 ). Given the mixed findings in the Indian banking context, this study seeks to provide a more systematic investigation of the SCP hypothesis using a panel VAR technique. This approach allows for the examination of long-term co-evolution between market structure, conduct, and financial performance, providing more powerful insights into the dynamics of the Indian banking sector. The use of panel Vector Autoregression (VAR) has become increasingly popular for exploring the dynamic relationships between key variables like market structure, conduct, and performance (Abrigo & Love, 2016 ; Mukhopadhyay & Chakraborty, 2016 ). Panel VAR allows for the examination of how changes in one variable (e.g., market concentration) affect others (e.g., interest rate spreads, profitability) over time. Methodology 4.1 Data The empirical analysis of this study employs an unbalanced panel dataset for the 30 Indian banks over the 2010–2020 period. The data was obtained from different sources the Reserve Bank of India’s Database on Indian Economy, the annual reports of the banks, and the PROWESS database from Centre for monitoring Indian economy (CMIE). The unbalanced nature of our panel accounts for mergers, acquisitions, and new entrants in the banking sector during the study period, reflecting the changing realities of the Indian banking landscape. 4.2 Variables Our model draws from the traditional Structure-Conduct-Performance (SCP) framework, dividing variables into three main categories: Structure, Conduct, and Performance. However, treating these categories as entirely separate is not possible, as the underlying factors often overlap. In the SCP model, market structure shapes industry performance through various channels, including buyer concentration, entry barriers, industry concentration, product differentiation, and cost structures. Conduct refers to how firms behave, shaped by their strategic responses to rivals' actions and market characteristics. We consider concentration(CR4), profitability (ROA), and Interest Rate Spread as indicators representing industry structure, performance, and conduct, respectively. 4.2.1 Concentration : We measure concentration by using the concentration ratio. The Concentration Ratio (CRₖ) is a simple and widely used measure of market concentration, which indicates the total market share controlled by the top k firms in an industry. It's particularly helpful for understanding the competitive landscape by focusing on the largest firms in the market $$\:CRₖ=\:\sum\:_{i=1}^{k}{MS}_{i}$$ Where k is the number of the largest firms in the market (the top 4), MS i is the market share of firm i , expressed as a percentage of the total. 4.2.2 Interest Rate Spread (IRS) : Interest Rate Spread is a critical measure in banking that reflects the difference between the average rate a bank charges on its loans and the average rate it pays on its deposits. In this study, we use IRS as a proxy for Conduct within the Structure-Conduct-Performance (SCP) paradigm. The justification for choosing IRS is that it depicts a perfect picture of a bank’s pricing mechanism, risk management strategy and competitive behaviour in the market. By changing the interest rate on both loan and deposit side, a bank not only impacts its profit, but at the same time strategically signals to the market about the level of competitive engagement; kind of clients the bank is interested in attracting; and the share of the market it would like to control. Interest Rate Spread (IRS) is calculated as the difference between the weighted average lending rate on all loans and the average cost of deposits . This measure gives a picture of the way in which the bank has behaved in relation to the rate of interest that they set for their loans and deposits. IRS = Weighted Average Interest Rate on Loans − Average Cost of Deposits Weighted Average Interest Rate on Loans shows the interest rate applied to the bank’s loan portfolio by considering the proportions and interest rates of the loan segments (for example, retail, corporate, personal). The weight of each segment is calculated based on the proportion of total loans, and the weighted average interest rate is obtained using the following formula: $$\:Weighted\:Average\:Interest\:Rate\:on\:Loans=\sum\:_{i=1}^{n}(\frac{{Loan\:Segment}_{i}}{Total\:Loans})\:\times\:\:{Interest\:Rate}_{i}$$ $$\:Average\:cost\:of\:Deposits=\:\frac{Total\:Interest\:Paid\:on\:Deposits}{Total\:Deposits}$$ Therefore, the IRS reflects the spread of what the bank earns from its loans and what it costs the bank to obtain deposits; in other words, it sums up the bank’s behaviour with respect to pricing policies and risk/reward balances in the banking sector. Higher IRS therefore points to more focus on profitability by charging higher prices for loans than the cost of deposits, while lower IRS signifies either offering lower prices in order to gain a wider market share (competitive pricing) or else holding lower-risk loans as a key strategy. 4.2.3 Performance : We use Return on Assets (ROA) and Tobin’s Q as our performance indicators in different systems of equations. ROA, an accounting measure, is calculated by dividing a company's earnings before interest and taxes (EBIT) by its total assets. Following Bertay, Demirgüç-Kunt, & Huizinga ( 2022 ), Tobin’s Q, based on stock market prices, is computed using the formula: $$\:Tobi{n}^{{\prime\:}}s\:Q=\:\frac{Market\:Value\:of\:Equity+Book\:Value\:of\:Preferred\:equity\:and\:Liabilities}{Book\:Value\:of\:Total\:Assets}$$ ROA is determined by dividing a bank's earnings before interest and taxes (EBIT) by its total assets. 4.3 Model To investigate the interconnections between market structure, conduct, and performance, we propose a three-equation model. Each endogenous variable—structure (CR4), conduct (IRS), and industry performance (measured by ROA)—is modeled as a function of its own past values, as well as past values of the other variables and an exogenous factor. CR4 it = α 1 CR4 it−1 + α 2 P it + α 3 P it−1 + α 4 P it−2 + α 5 P it−3 + α 6 IRS it + α 7 IRS it−1 + α 8 IRS it−2 + α 9 IRS it−3 + ε it IRS it = β 1 IRS it−1 + β 2 P it + β 3 P it−1 + β 4 P it−2 + β 5 P it−3 + β 6 CR4 it + β 7 CR4 it−1 + β 8 CR4 it−2 + β 9 CR4 it−3 + ε it P it = γ 1 P it−1 + γ 2 CR4 it + γ 3 CR4 it−1 + γ 4 CR4 it−2 + γ 5 CR4 it−3 + γ 6 IRS it + γ 7 IRS it−1 + γ 8 IRS it−2 + γ 9 IRS it−3 + ε it where P it is performance (measured by ROA); IRS it is Interest rate spread (conduct); CR4 it is concentration (structure). Given the endogeneity of all but one variable in this study, we use a panel Vector Autoregression (PVAR) model. This method is suitable for analysing causal relationships among endogenous variables in dynamic systems (Mukhopadhyay & Chakraborty, 2016 ). PVAR accounts for dynamic interactions between variables through lagged terms within and across time series. To apply PVAR, we first verify the stationarity of the variables. Using panel unit root tests (Im et al., 2003 ; Levin et al., 2002 ), we confirm that our panel data contains no unit roots (Table 1 ) Table 1 Unit root Test for variables Variable Method Statistic Prob.** ROA Levin, Lin & Chu t* -5.44846 0 Im, Pesaran and Shin W-stat -3.26403 0.0006 ADF-Fisher Chi-square 75.2095 0.0035 CR4 Levin, Lin & Chu t* -5.17817 0 Im, Pesaran and Shin W-stat -1.7672 0.037 ADF-Fisher Chi-square 71.6555 0.0043 IRS Levin, Lin & Chu t* -5.35052 0 Im, Pesaran and Shin W-stat -2.76055 0.0029 ADF-Fisher Chi-square 74.6546 0.0039 Automatic lag length selection based on Schwarz information criterion (SIC) Empirical Results 5.1 Unit Root Tests The results of the panel unit root tests (Table 1 ) indicate that all variables (CR4, IRS, ROA, and Tobin's Q) are stationary at levels. This satisfies the prerequisite for estimating the VAR model without the need for differencing. 5.2 Lag Order Selection Tables 2 and 4 present the lag order selection criteria for the models with ROA and Tobin's Q, respectively. For both models, the majority of the criteria (LR, FPE, HQ, and AIC) suggest an optimal lag length of 3. We therefore proceed with a lag order of 3 for both specifications. Table 2 Lag order selection (CR4, IRS, and ROA as endogenous) Lag LogL LR SC FPE HQ AIC 0 896.102 NA −7.756031 8.35e-08 −7.783866 −7.801015 1 1694.215 1568.124 −14.51036∗ 8.48e-11 −14.61771 −14.69030 2 1710.045 30.05214 −14.43245 8.02e-11 −14.62031 −14.74732 3 1733.476 45.04512∗ −14.42431 7.02e-11∗ −14.69268∗ −14.87415∗ LR sequential modified LR test statistic (each test at 5% level), SC Schwarz information criterion, FPE final prediction error, HQ Hannan–Quinn information criterion, AIC Akaike information criterion, ∗ Lag order selected by the criterion Table 3 P-VAR estimates (CR4, IRS, and ROA as endogenous) Variable CR4 IRS ROA CR4 (− 1) 1.354702 0.015673 −0.106302 (0.05742) (0.00975) (0.06711) ((23.5875)) ((1.60790)) ((− 1.58457)) CR4 (− 2) −0.192044 −0.011752 0.091251 (0.05114) (0.00864) (0.05935) ((− 3.75531)) ((− 1.36030)) ((1.53898)) CR4 (− 3) −0.176934 −0.003621 0.017145 (0.02852) (0.00481) (0.03297) ((− 6.20532)) ((− 0.75296)) ((0.51998)) IRS (− 1) 0.149032 0.836901 1.238692 (0.41355) (0.07102) (0.48413) ((0.36024)) ((11.7845)) ((2.55879)) IRS (− 2) −0.164775 −0.048927 −0.715841 (0.54720) (0.09318) (0.63891) ((− 0.30111)) ((− 0.52521)) ((− 1.12043)) IRS (− 3) 0.060271 0.094458 −0.138745 (0.36901) (0.06297) (0.42915) ((0.16333)) ((1.50082)) ((− 0.32340)) ROA (− 1) −0.062201 −0.000614 0.848462 (0.05935) (0.01008) (0.06922) ((− 1.04819)) ((− 0.06091)) ((12.2585)) ROA (− 2) −0.009382 0.002385 −0.074251 (0.07481) (0.01302) (0.08752) ((− 0.12541)) ((0.18326)) ((− 0.84845)) ROA (− 3) 0.052818 0.011231 0.104573 (0.05625) (0.00971) (0.06587) ((0.93873)) ((1.15661)) ((1.58743)) R-squared 0.963512 0.932084 0.744527 Adj. R-squared 0.962321 0.929461 0.734331 () Contain Standard errors, (()) contain t-statistic Table 4 Unit root Test for Tobin’s Q Variable Method Statistic Prob.** TOBIN’S_Q Levin, Lin & Chu t* -3.89129 0 Im, Pesaran and Shin W-stat -2.5034 0.0061 ADF-Fisher Chi-square 63.3399 0.0385 Automatic lag length selection based on Schwarz information criterion (SIC) ---------------------- Insert Table 1 ---------------------- ---------------------- Insert Table 2 ---------------------- 5.3 Model with ROA The results in Table 3 reveal several interesting relationships: Market Structure (CR4) exhibits strong persistence, with its first lag having a significant positive effect (1.354702) on current CR4. The second and third lags show significant negative effects, suggesting a cyclical pattern in market concentration. Conduct (IRS) also shows strong persistence, with its first lag having a significant positive effect (0.836901) on current IRS. Interestingly, the first lag of CR4 has a positive but insignificant effect on IRS, suggesting that increased market concentration may lead to higher interest rate spreads, albeit weakly. Performance (ROA) demonstrates high persistence, with its first lag having a significant positive effect (0.848462) on current ROA. The first lag of IRS has a significant positive effect (1.238692) on ROA, indicating that higher interest rate spreads lead to improved profitability in the short term. ---------------------- Insert Table 3 ---------------------- 5.4 Robustness Check When we replace the ROA with Tobin’s Q as a measure of performance, the results remain similar. First we again check for the unit root (Table 4 ) and choose the appropriate lag order (Table 5 ). The results in Table 6 largely corroborate the findings from the ROA model, with some notable differences: Market Structure (CR4) persistence remains strong, with coefficients similar to those in the ROA model. The persistence of IRS is slightly lower (0.804912) compared to the ROA model, but still significant. Tobin's Q shows lower persistence (0.608291) compared to ROA. Interestingly, the first lag of CR4 has a positive but insignificant effect on Tobin's Q, suggesting that market concentration may have a weak positive impact on market valuation. Table 5 Lag order selection (CR4, IRS and TOBIN’S_Q as endogenous) Lag LogL LR SC FPE HQ AIC 0 409.670 NA −3.506 5.81e-06 −3.533 −3.551 1 1137.495 1430.235 −9.647∗ 1.09e-08 −9.755 −9.828 2 1152.398 29.215 −9.565 1.04e-08 −9.753 −9.880 3 1183.502 59.412∗ −9.623 8.52e-09∗ −9.891∗ −10.072∗ LR sequential modified LR test statistic (each test at 5% level), SC Schwarz information criterion, FPE final prediction error, HQ Hannan–Quinn information criterion, AIC Akaike information criterion, ∗ Lag order selected by the criterion Table 6 P-VAR estimates (CR4, IRS, and TOBIN’S_Q as endogenous) Variable CR4 IRS TOBIN’S_Q CR4 (− 1) 1.332105 0.006702 1.012543 (0.06023) (0.01013) (0.78942) ((22.1162)) ((0.66136)) ((1.28256)) CR4 (− 2) −0.184602 −0.004582 −0.628901 (0.05128) (0.00852) (0.68893) ((− 3.59972)) ((− 0.53775)) ((− 0.91225)) CR4 (− 3) −0.176194 −0.004873 0.532171 (0.02796) (0.00484) (0.37389) ((− 6.30102)) ((− 0.96887)) ((1.42302)) IRS (− 1) 0.136892 0.804912 5.298021 (0.41381) (0.06915) (5.46521) ((0.32116)) ((11.6432)) ((0.97044)) IRS (− 2) −0.240219 −0.035218 0.164211 (0.52982) (0.08921) (7.01135) ((− 0.45371)) ((− 0.39474)) ((0.02342)) IRS (− 3) 0.065784 0.105891 −0.758944 (0.35918) (0.06143) (4.78317) ((0.18084)) ((1.72433)) ((− 0.15861)) TOBIN’S_Q (− 1) −0.000509 0.000746 0.608291 (0.01043) (0.00091) (0.06827) ((− 0.05523)) ((0.81918)) ((9.09248)) TOBIN’S_Q (− 2) −0.001664 0.000214 0.334812 (0.00584) (0.00102) (0.07789) ((− 0.28503)) ((0.20980)) ((4.29817)) TOBIN’S_Q (− 3) −0.000353 0.001615 −0.182419 (0.00524) (0.00088) (0.06814) ((− 0.06734)) ((1.83247)) ((− 2.67802)) R-squared 0.963123 0.930642 0.748421 Adj. Squared 0.961605 0.927916 0.737813 () Contain Standard errors, (()) contain t-statistic ---------------------- Insert Table 4 ---------------------- ---------------------- Insert Table 5 ---------------------- --------------------- Insert Table 6 ---------------------- Discussion This study partially supports the SCP hypothesis, more specifically regarding the relationship between market concentration (CR4) and interest rate spreads (IRS). Profitability persistence in the Indian banking sector is consistent with the SCP framework, implying that banks in more concentrated markets enjoyed sustained profitability, supporting the arguments of Sinha and Sharma ( 2015 ). This is consistent with the idea that banks can set prices to maximize short term profits as demonstrated by Banerjee and Savitha ( 2021 ) in the Indian life microinsurance market. The somewhat positive correlation between CR4 and IRS, however, implies that competition is still a moderating factor. According to O'Connell (2023) and Sahile et al. ( 2015 ), management effectiveness and macroeconomic conditions, as well as market concentration, may play important roles in explaining profitability. This is consistent with VanHoose's (2022) analysis of how regulatory actions and efficiency improvements may upend concentrated market structures and cast doubt on the concentration's long-term consequences on profitability. As in Mateev et al. ( 2021 ), the cyclical pattern of market concentration in this study also accords with findings that competition affects the behaviour of banks, especially during crisis. He found that competition had pushed banks to raise their capital positions in the MENA region to manage risks, a pattern also reflected in our findings of persistent profitability in concentrated markets. Similar to Shair et al. ( 2019 ) and Saif-Alyousfi ( 2022 ), the role of competition in compressing profitability can be seen in the Indian banking sector. Market concentration may improve short run profitability but competitive pressures remain a factor in pricing strategy. This is consistent with Alhassan et al. ( 2015 ) in who in Ghanaian context found that efficiency, not concentration, was the main determinant of profitability. Our findings also indicate that while profitability (ROA) is enhanced in the short term by higher interest rate spreads, these gains are not fully reflected in market-based performance measures, such as Tobin’s Q. An alternative explanation is that market expectations of future competition or regulatory intervention are at play (Sinha and Sharma 2015 ). Overall, the results support neither the SCP nor the efficient structure hypothesis, but provide a partial support for both. The strong positive relation between lagged CR4 with current CR4, shows that market structure appears to persist over time, matching with the findings of Berger and Hannan ( 1989 ) who found that market structure in banking is usually stable. The negative coefficients on the second and third lags of CR4, however, indicate a cyclical pattern that may be due to regulatory or competitive forces that periodically break up established market structures. The positive, albeit weak, relationship between lagged CR4 and IRS suggests that higher market concentration can lead to less competitive pricing, supporting the SCP hypothesis to some extent. This finding is in line with Bain’s ( 1951 ) SCP theory, though the weak statistical significance mirrors Goldberg and Rai’s ( 1996 ) results in European banking, where limited support for traditional SCP dynamics was observed. The significant positive effect of lagged IRS on ROA supports the efficient structure hypothesis (Demsetz, 1973 ), indicating that banks with market power can extract higher rents through higher spreads. However, the insignificant effect of IRS on Tobin's Q implies that the market does not value these short-term gains, likely anticipating future competitive or regulatory adjustments. The persistence of ROA compared to Tobin's Q suggests that accounting-based measures of performance are more stable than market-based measures in the Indian banking sector. The forward-looking nature of Tobin's Q, which incorporates market expectations of future performance and regulatory changes, may explain this discrepancy. The cyclical patterns observed in CR4 coefficients likely reflect the structural reforms in the Indian banking sector, including mergers of public sector banks and the entry of new private banks. Conclusion, implications and future research This study provides partial support for the Structure-Conduct-Performance (SCP) hypothesis in the Indian banking sector, particularly in the relationship between market concentration and profitability. Our findings indicate that banks operating in more concentrated markets tend to enjoy sustained profitability in the short term, as reflected by the positive relationship between market concentration (CR4) and interest rate spreads (IRS). However, competition continues to moderate this relationship, and the effects of concentration are not fully captured by market-based performance measures like Tobin's Q. This suggests that market participants anticipate future competition or regulatory intervention, which could mitigate the benefits of concentration over time. Additionally, the cyclical nature of market concentration observed in the data points to the role of regulatory interventions and competitive forces in periodically disrupting established market structures. While the SCP hypothesis receives partial validation, the efficient structure hypothesis also plays a critical role in explaining bank profitability, particularly through the ability of more efficient banks to extract higher rents via interest rate spreads. The results have several implications for policymakers and banking institutions. First, the persistence of market concentration suggests that consolidation efforts, such as bank mergers, may reinforce market power and contribute to short-term profitability. However, regulators need to ensure that this concentration does not lead to excessive pricing power, which could harm consumer welfare. The weak relationship between concentration and pricing observed in this study underscores the importance of regulatory oversight to maintain competitive pressures in the sector. For banks, the findings highlight the importance of efficiency and strategic management. While concentration can lead to short-term profitability, sustained performance depends on the ability to manage competition and navigate regulatory changes. Banks should focus on improving operational efficiency to remain competitive in a dynamic market environment. Several avenues for future research emerge from this study. First, further exploration of the cyclical nature of market concentration is warranted, particularly in light of ongoing regulatory reforms in the Indian banking sector. Future studies could examine the long-term effects of these structural changes, particularly in relation to market power, pricing strategies, and profitability. Second, the relationship between market concentration and risk-taking behaviour could be explored further. Studies such as Mateev et al. ( 2021 ) have shown that competition can influence capital allocation and risk management strategies. Research focusing on how concentration impacts banks' risk profiles in India, especially under different regulatory regimes, could provide valuable insights. Finally, the role of digitalization and technological disruption in the banking sector should be considered in future studies. As digital banking and fintech continue to reshape the competitive landscape, understanding how these innovations affect market structure, pricing, and profitability will be crucial for both researchers and practitioners. Declarations Disclosure of Interest : The authors report no conflict of interest. Data availability The data that support the findings of this study are publicly available in raw form. However, the specific variable calculations unique to this work cannot be shared until the Author 2 completes here doctoral work and may be made available on reasonable request. Funding The research has no funding support Authors’ Contributions Dilawar Ahmad Bhat led the study's conceptualization, data analysis, and manuscript drafting, managing the overall research direction and collaboration among authors. Shahida Rasheed conducted the literature review, assisted with data collection, and supported preliminary analyses. Himanshu Seth contributed expertise in statistical techniques, supporting the study's methodology and result interpretation. Irshad Ahmad Malik provided feedback on drafts, ensuring contextual relevance and alignment with research aims. All authors reviewed and approved the final manuscript. Acknowledgements We would like to express our sincere gratitude to our respective institutions—Symbiosis School of Banking and Finance, Pune; University of Kashmir, Srinagar; and IIM Rohtak, Haryana—for their support throughout this research. We also appreciate the constructive feedback from our peers, which helped us enhance the rigour and clarity of this study. References Abrigo, M. R., & Love, I. (2016). Estimation of panel vector autoregression in Stata. The Stata Journal, 16 (3), 778–804. https://doi.org/10.1177/1536867X1601600314 Alhassan, A. L., Tetteh, M. L., & Brobbey, F. O. (2015). Market power efficiency and bank profitability: Evidence from Ghana. Economic Change and Restructuring, 49(1), 71-93. https://doi.org/10.1007/s10644-015-9174-6 Ansari, J. and Goyal, A. 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Berger, A.N., Demsetz, R.S., & Strahan, P.E. (1999). The consolidation of the financial services industry: causes, consequences, and implications for the future. Journal of Banking and Finance , 23(2–4), 135–194. Bertay, A. C., Demirgüç-Kunt, A., & Huizinga, H. (2022). Are international banks different? Evidence on bank performance and strategy. Journal of Financial Services Research. https://doi.org/10.1007/s10693-022-00390-3 Boyd, J.H., & De Nicolo, G. (2005). The theory of bank risk-taking and competition revisited. Journal of Finance , 60(3), 1329–1343. Buch, C., & Dages, B. G. (2018). Structural changes in banking after the crisis (CGFS Papers No. 60). Bank for International Settlements. https://www.bis.org/cgfs/publ/60.htm Claessens, S., & Laeven, L. (2004). What drives bank competition? Some international evidence. Journal of Money, Credit and Banking, 36 (3), 563–583. Clarke, R., & Davies, S. W. (1982). Market structure and price-cost margins. Economica , 49, 277–287. Davies, S., Lyons, B., Dixon, H., & Geroski, P. (1989). Surveys in economics: Economics of industrial organisation . London: Longman. Delis, M.D., Molyneux, P., & Pasiouras, F. (2011). Regulations and productivity growth in banking: evidence from transition economies. Journal of Money, Credit and Banking , 43(4), 735–764. Demsetz, H. (1973). Industry structure, market rivalry, and public policy. The Journal of Law and Economics, 16 (1), 1–9. Evanoff, D. D., & Fortier, D. L. (1988). Re-evaluation of the structure-conduct performance paradigm in banking. Journal of Financial Services Research, 1 (3), 277–294. Goldberg, L. G., & Rai, A. (1996). The structure-performance relationship for European banking. Journal of Banking & Finance, 20 (4), 745–771. Gonzalez, F. (2005). Bank regulation and risk-taking incentives: an international comparison of bank risk. Journal of Banking and Finance , 29(5), 1153–1184. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115 (1), 53-74. Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108 (1), 1-24. Mason, E. (1939). Price and production policies of large-scale enterprises. American Economic Review, 29 (1), 61–74. Mateev, M., Tariq, M. U., & Sahyouni, A. (2021). Competition, capital growth, and risk-taking in emerging markets: Policy implications for banking sector stability during COVID-19 pandemic. PLoS ONE, 16(6). https://doi.org/10.1371/journal.pone.0253803 Mishra, P., & Sahoo, D. (2012). Structure, conduct, and performance of the Indian banking sector. Revecp, 12 (4), 235–264. https://doi.org/10.2478/v10135-012-0011-9 Mohan, R. (2006). Financial sector reforms and monetary policy: The Indian experience. Paper presented at the Conference on Economic Policy in Asia at Stanford , Stanford Center for International Development and Stanford Institute for Economic Policy Research. http://www.rakeshmohan.com/docs/RBIBulletinJuly2006-1.pdf Mukhopadhyay, J., & Chakraborty, C. (2016). Competition and industry performance: A panel VAR analysis in the Indian manufacturing sector. Journal of Industry, Competition and Trade, 15 (1), 1–20. https://doi.org/10.1007/s40953-016-0055-2 O’Connell, M. (2023). Bank-specific, industry-specific, and macroeconomic determinants of bank profitability: Evidence from the UK. Studies in Economics and Finance, 40(1), 155-174. https://doi.org/10.1108/SEF-10-2021-0413 Rastogi, S., Sharma, A., Pinto, G., & Bhimavarapu, V.M. (2022). A literature review of risk, regulation, and profitability of banks using a scientometric study . Future Business Journal, 8(1), 28. https://doi.org/10.1186/s43093-022-00146 Sahile, G. S. W., Tarus, D. K., & Cheruiyot, T. K. (2015). Market structure-performance hypothesis in Kenyan banking industry. International Journal of Emerging Markets, 10(4), 697-710. https://doi.org/10.1108/IJoEM-12-2012-0178 Saif-Alyousfi, A. Y. H. (2022). Determinants of bank profitability: Evidence from 47 Asian countries. Journal of Economic Studies, 49(1), 44-60. https://doi.org/10.1108/JES-05-2020-0215 Scherer, F. M., & Ross, D. (1990). Industrial market structure and economic performance . Boston: Houghton Mifflin. Schmalensee, R. (1987). Collusion versus differential efficiency: Testing alternative hypotheses. Journal of Industrial Economics, 35 , 399–425. Schumpeter, J. A. (1942). Capitalism, socialism, and democracy . New York: Harper & Row. Schwerter, A. (2012). Basel III’s ability to mitigate systemic risk. Journal of Banking Regulation , 14(4), 286–310. Shair, F., Sun, N., & Shaorong, S. (2019). Impacts of risk and competition on the profitability of banks: Empirical evidence from Pakistan. PLoS ONE, 14(11). https://doi.org/10.1371/journal.pone.0224378 Shepherd, W. G. (1982). Causes of increased competition in the US economy, 1939–1980. The Review of Economics and Statistics, 64 (4), 613–626. Sinha, P., & Sharma, S. (2015). Determinants of bank profits and its persistence in Indian Banks: A study in a dynamic panel data framework. International Journal of System Assurance Engineering and Management, 7(1), 35-46. https://doi.org/10.1007/s13198-015-0388-9 Smirlock, M. (1985). Evidence on the (non) relationship between concentration and profitability in banking. Journal of Money, Credit, and Banking, 17 (1), 69–83. Tabak, B. M., Gomes, G. M. R., & Medeiros Júnior, M. S. (2012). The impact of market power at bank level in risk-taking: The Brazilian case (Working Paper No. 283). The Banco Central do Brasil. https://www.bcb.gov.br/pec/wps/ingl/wps283.pdf Tirole, J. (1988). The theory of industrial organization . Cambridge: The MIT Press. VanHoose, D. (2022). The Industrial Economics of Banking (3rd ed.). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-16241-1_4 Footnotes https://www.thehindubusinessline.com/money-and-banking/public-sector-banks-may-see-further-consolidation/article67647634.ece Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2025 Read the published version in Journal of Economic Structures → Version 1 posted Editorial decision: Minor revision 20 Sep, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 07 Nov, 2024 First submitted to journal 06 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5406984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379055393,"identity":"781f8c8d-9b6b-47c4-a580-e8a3a2436c2d","order_by":0,"name":"Dilawar Ahmad Bhat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFCCxMYDYJq9AUgYWBClpQGihQdEGUgQoyWBAaJFIgFMEtbA357ccOBnm12+/MznVzf8KJAAinQn4NUiceZhw8HetmTLDbdzym72AB0mcebsBvzW3AD6hXcbs4GBdE7aDR6gFgOJXPxa5IFaDv7dVm8gP/NM2s0/xGgxAGo5zLvtsAHDDfZjt4myxRDol8Oy/44bGJzJYbstYyDBQ9AvcsfTHz58c6baQL79+LObb/7YyPG39xLwPgLwGIBJYpWDAPsDUlSPglEwCkbBCAIAJ5JO/tGEvtoAAAAASUVORK5CYII=","orcid":"","institution":"Symbiosis International (Deemed University) Symbiosis School of Banking \u0026 Finance","correspondingAuthor":true,"prefix":"","firstName":"Dilawar","middleName":"Ahmad","lastName":"Bhat","suffix":""},{"id":379055394,"identity":"ab5d6e5b-3450-4e71-a442-1e0862b6ebb0","order_by":1,"name":"Himanshu Seth","email":"","orcid":"","institution":"IIM Rohtak: Indian Institute of Management Rohtak","correspondingAuthor":false,"prefix":"","firstName":"Himanshu","middleName":"","lastName":"Seth","suffix":""},{"id":379055395,"identity":"8e0fad45-3fa9-4ea0-8028-00e12e38e05f","order_by":2,"name":"Shahida Rasheed","email":"","orcid":"","institution":"University of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Shahida","middleName":"","lastName":"Rasheed","suffix":""},{"id":379055396,"identity":"10dbfebf-4bc3-4f63-8cda-f0517d4acb43","order_by":3,"name":"Irshad Ahmad Malik","email":"","orcid":"","institution":"University of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Irshad","middleName":"Ahmad","lastName":"Malik","suffix":""}],"badges":[],"createdAt":"2024-11-07 05:52:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5406984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5406984/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40008-025-00361-6","type":"published","date":"2025-10-31T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95041361,"identity":"a01139e7-c493-490d-9fa7-1b6859f977f2","added_by":"auto","created_at":"2025-11-03 16:11:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":988008,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5406984/v1/b5289110-cfc8-43bb-b89b-48ef24b71a16.pdf"}],"financialInterests":"","formattedTitle":"Testing SCP hypothesis amid growing consolidation in the Indian banking sector","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the economic reforms of 1991, the Indian banking sector has been on a path of development, growth, and reform. The remarkable reform measures undertaken include major easing of financial repression; doing away with interest rate controls, entry of private sector banks, more functional and operational autonomy to public financial institutions, laying the roadmap for financial integration and globalization, and bolstering the safety mechanisms for stability of the financial system\u0026nbsp;by adopting capital adequacy norms (Mohan, 2006).\u003c/p\u003e\n\u003cp\u003eThe arrival of new private banks and the proliferation of Non-Banking Financial Companies (NBFCs) have disrupted the traditional contours of competition, leading to a radical reconfiguration of the market\u0026apos;s structure.\u003c/p\u003e\n\u003cp\u003eSimultaneously, a wave of consolidations has swept through the sector, merging entities into larger, more formidable institutions[1], thus concentrating market power and altering the competitive dynamics that once prevailed. These structural shifts have been mirrored by significant changes in market conduct. Over the past two decades, there has been massive consolidation in the Indian banking sector hence leading to a better banking system. The process started with the amalgamation of ICICI Bank and ICICI Limited in the year 2002 and Bank of Punjab merged with Centurion Bank during the year 2004. The decade of 2010 also witnessed consolidation where State Bank of Indore merged with the SBI and Kotak Mahindra Bank acquired ING Vysya Bank in 2015. A significant increase in mergers started since 2017 when SBI merged with five of its associates as well as Bharatiya Mahila Bank; PNB with Oriental Bank of Commerce and United Bank of India, Canara Bank with Syndicate Bank; Union Bank of India amalgamating with Andhra Bank and Corporation Bank; Allahabad Bank merged with Indian Bank. These mergers have shrunk the number of public sector banks from 27 as of the end of year 2017 to 12 by the end of year 2020. Overall, the number of banks has gradually declined, be they private or public ones, which have resulted in the creation of fewer but better banks able to foster the economy\u0026rsquo;s growth and compete at the international level.\u003c/p\u003e\n\u003cp\u003eDigital banking innovation has altered the existing banking paradigms, as the use of fintech innovations particularly UPI provides a new dimension of customer relations. In addition, with the divestment of Development Financial Institutions (DFIs) and the adoption of Insolvency and Bankruptcy Code (IBC) there are new constraints on banking practices \u0026ndash; particularly risk mitigation and NPA management. The IBC has specifically introduced a new discipline in the sector forcing the banks to transform its lending mechanisms by mitigating forbearance lending\u003c/p\u003e\n\u003cp\u003eAll these changes have implications for the performance of the banking sector in the country. The intense competition from private banks and NBFCs along with new technology have compelled the banks to be more productive but simultaneously it posed new threats to the profit margins and market shares. Also, the mergers have led to the emergence of stronger banks that can operate under changing economic systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus, the Structure-Conduct-Performance (S-C-P) paradigm appears to be particularly useful in such a dynamic context. This paper uses a panel VAR to examine the changes in market conduct brought by the structural transformation of the banking industry and its impact on performance consequences. In examining these dynamics, this study aims to shed light on the continued transformation of the Indian banking system and its implications for regulations and future strategies.\u003c/p\u003e\n"},{"header":"Background Theory","content":"\u003cp\u003ePrevious research into the dynamics between market structure and performance revolves around two main theories namely the Structure-Conduct-Performance (SCP) hypothesis and the Efficient Structure Hypothesis (ESH). According to the SCP hypothesis, a rise in the market concentration in the banking industry results into an increase in the market power which, in turn, enhances pricing and profitability due to collusion opportunities. While the EHS hypothesis posits that This process works in reverse, that is, cutting-edge technology or superior management practices drive firms to become more profitable and thus more concentrated\u003c/p\u003e \u003cp\u003eThe SCP framework based on the works of Mason (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1939\u003c/span\u003e) and Bain (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1951\u003c/span\u003e) postulates that the conduct and performance are the internal factors while the market structure is determined by external factors. This theory suggests that concentrated banking industry increases the market power, where banks can gain monopolistic profits by offering lower deposit rates and charging higher loan rates. Market structure here is usually measured with the help of indicators like concentration ratio or the Hirschman-Herfindahl Index. On the other hand, the ESH, as introduced by Demsetz (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) posits that market concentration emerges in a reverse fashion whereby efficient firms tend to secure bigger market shares. Companies that perform well by having superior technology or managers are more productive and hence earn more profits which translate to a larger market share that is a characteristic of increased concentration. Thus, according to This perspective, efficiency of banks is the primary factor influencing market concentration.\u003c/p\u003e \u003cp\u003eRegarding the ESH, studies by Smirlock (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), Evanoff and Fortier (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), and Berger (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) support the hypothesis, showing that higher profits are associated with larger market shares, without a direct causal link between concentration and profitability. Berger (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) finds only weak evidence of a positive relationship between efficiency and market power, while Berger and Hannan (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) indicate that efficiency negatively impacts market structure in U.S. banks.\u003c/p\u003e \u003cp\u003eThe traditional Structure-Conduct-Performance (SCP) paradigm, rooted in neoclassical theory, has faced significant critique. Critics like Davies et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) argue that SCP lacks a rigorous theoretical foundation, while Scherer and Ross (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) contend that its deterministic nature fails to capture the complexities of imperfect markets. The most pointed criticism, however, is SCP's unidirectional causality, which later literature challenges by suggesting a two-way relationship between structure, conduct, and performance. Demsetz (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) offers the Efficient Structure Hypothesis (ESH) as an alternative, arguing that market concentration results from superior firm efficiency rather than leading to it. Shepherd (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) presents empirical evidence about Relative-Market-Power Hypothesis (RMP) that suggests that the firms with greater market shares and differentiated products, have greater market power and profitability. This is taken a notch higher by Schmalensee (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) in the Hybrid Collusion-Efficiency Hypothesis where he combines both SCP and ESH to advance the notion that both concentration and efficiency affect performance. SCP is countered by Baumol et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) with the theory of market contestability which looks at the threat of competition rather than structure. However, Schumpeter (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1942\u003c/span\u003e) along with other post-classical economists of the Austrian School of Economics, move the lens of focus to dynamic competition, claiming that competition is a process not a structure. The subject on game theory and Nash equilibrium during the period of 1980s and 1990s, as with the works of Clarke and Davies (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) and Tirole (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), shifted the focus from the structure to the conduct in industrial organization. However, SCP is still a valuable model, especially, where the governmental regulations are well developed and affect the structure.\u003c/p\u003e \u003cp\u003eThus, in the context of a heavily regulated industry like the Indian Banking Industry, the Structure Conduct Performance (SCP) paradigm is a more appropriate tool for analysis than the Efficient Structure Hypothesis (ESH). Since regulatory bodies, particularly RBI, have a massive impact on the market, the SCP framework, which explains how the structure of the market depending on the external conditions, including regulatory policies, affects the behaviour and performance of the banks, is quite suitable.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eTheoretical literature suggests that consolidation shows that it has implications on the trade-off between profit, control and risk. It has been seen that the banks having higher levels of market concentration have lower levels of competition and therefore can be more profitable but at the same time the situation of monopolistic competition that prevails in such markets can lead to systemic instability unless properly managed. Some researchers are of the view that market power enables banks to undertake fewer risks and this enhances stability (Tabak et al., 2015). Nonetheless, critics argue that a decrease in competition leads to inefficiency and can hamper innovation (Boyd and De Nicolo, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The relationship between market power and risk-taking is widely debated. Ariss (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Berger et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) opine that increased concentration of banks makes them averse to risk particularly under capital regulation While Beck et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) notes that extreme concentration may cause moral hazard where institutions feel they are too big to fail they can engage in riskier activities.\u003c/p\u003e \u003cp\u003eThere is also controversy regarding the relationship between regulation and profitability: some of the researches indicate that high regulation hinders profitability while others affirm that if regulations are properly developed they help to provide stability profitable in the long run (Delis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Basel III for example was meant to strengthen the banking sector but it reduced liquidity and made operations more complicated according to Schwerter (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInternationally literature has documented studies that relates market concentration, competition and bank profitability. In the study conducted by O\u0026rsquo;Connell (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) for the UK banking sector, the SCP hypothesis has been rejected; however, macro factors like interest rate and inflation were identified to have a direct influence over profitability. Similarly, in Kenya, Sahile et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that efficiency, rather than market concentration, was the primary driver of profitability, challenging the traditional SCP framework. VanHoose (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) supplemented the previous discussion of market power and efficiency demonstrating that while scale expansion can lead to competitive pricing, concentrated markets can still have complex dynamics between market structure and behaviour.\u003c/p\u003e \u003cp\u003eMateev et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed that higher competition in the MENA region corresponds to the higher capital ratio and curbed risky behaviour during the COVID-19 outbreak; thus, competition plays a part in moderating performance results. Shair et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) proved that in the context of Pakistan, the liquidity risk had a positive impact on the profitability but the competition was found to be detrimental to the profitability. SCP hypothesis was supported by Saif-Alyousfi (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) among 47 Asian countries as he confirmed that market concentration has a positive effect on the firm\u0026rsquo;s profitability during financial crisis. SCP hypothesis was rejected by Alhassan et al (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) who established that technical efficiency was the primary determinant of profitability in the context of Ghanaian firms.\u003c/p\u003e \u003cp\u003eIn the Indian context, previous studies about the relationship between the market structure and performance, have yielded inconclusive results. The studies of Sinha and Sharma (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) provides evidence of moderate level of profit persistence in the Indian banks in support of the SCP hypothesis and capital adequacy and operating efficiency have emerged as key drivers of profitability. Similarly, Mishra and Sahoo (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) have endorsed SCP hypothesis and examined the influential relationships between market structure, banking conduct and performance. Contrary to the SCP hypothesis, Barua, Roy, and Raychaudhuri (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found a negative relationship between market concentration and profitability. They found capitalization, credit risk and leverage as the primary determinants of bank profitability. Ansari and Goyal (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) examined the relationship interest rate spreads and market concentration, finding that increased competition lowers loan rates and spreads, but managerial inefficiency and regulatory constraints can exacerbate spreads. Regulatory frameworks, such as those implemented by the RBI, have aimed to strengthen banks' competitiveness and resilience in the face of global turbulence (Barth et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In the micro-insurance sector, Banerjee and Savitha (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that firms in the Indian life microinsurance industry performed better when competitive pressures were lower, supporting the SCP hypothesis. Their findings emphasize the significance of market structure in determining profitability in niche markets.\u003c/p\u003e \u003cp\u003eLastly, the Indian banking industry has changed its structure in terms of consolidation and regulatory reforms within a short span of time and hence altered the competitive landscape comprehensively. This has been evidenced by improved Concentration ratios and the Herfindahl-Hirschman Index (HHI) ratios over time driven by the RBI\u0026rsquo;s intention to purge the industry of weaker players. While consolidation improves the balance sheet resilience and operating performance, it can hamper competition and innovation (Buch \u0026amp; Dages, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the mixed findings in the Indian banking context, this study seeks to provide a more systematic investigation of the SCP hypothesis using a panel VAR technique. This approach allows for the examination of long-term co-evolution between market structure, conduct, and financial performance, providing more powerful insights into the dynamics of the Indian banking sector.\u003c/p\u003e \u003cp\u003eThe use of panel Vector Autoregression (VAR) has become increasingly popular for exploring the dynamic relationships between key variables like market structure, conduct, and performance (Abrigo \u0026amp; Love, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mukhopadhyay \u0026amp; Chakraborty, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Panel VAR allows for the examination of how changes in one variable (e.g., market concentration) affect others (e.g., interest rate spreads, profitability) over time.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Data\u003c/h2\u003e\n \u003cp\u003eThe empirical analysis of this study employs an unbalanced panel dataset for the 30 Indian banks over the 2010\u0026ndash;2020 period. The data was obtained from different sources the Reserve Bank of India\u0026rsquo;s Database on Indian Economy, the annual reports of the banks, and the PROWESS database from Centre for monitoring Indian economy (CMIE). The unbalanced nature of our panel accounts for mergers, acquisitions, and new entrants in the banking sector during the study period, reflecting the changing realities of the Indian banking landscape.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Variables\u003c/h2\u003e\n \u003cp\u003eOur model draws from the traditional Structure-Conduct-Performance (SCP) framework, dividing variables into three main categories: Structure, Conduct, and Performance. However, treating these categories as entirely separate is not possible, as the underlying factors often overlap. In the SCP model, market structure shapes industry performance through various channels, including buyer concentration, entry barriers, industry concentration, product differentiation, and cost structures. Conduct refers to how firms behave, shaped by their strategic responses to rivals\u0026apos; actions and market characteristics.\u003c/p\u003e\n \u003cp\u003eWe consider concentration(CR4), profitability (ROA), and Interest Rate Spread as indicators representing industry structure, performance, and conduct, respectively.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.2.1 Concentration\u003c/strong\u003e: We measure concentration by using the concentration ratio. The Concentration Ratio \u003cem\u003e(CRₖ)\u003c/em\u003e is a simple and widely used measure of market concentration, which indicates the total market share controlled by the top \u003cem\u003ek\u003c/em\u003e firms in an industry. It\u0026apos;s particularly helpful for understanding the competitive landscape by focusing on the largest firms in the market\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:CRₖ=\\:\\sum\\:_{i=1}^{k}{MS}_{i}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cem\u003ek\u003c/em\u003e is the number of the largest firms in the market (the top 4), \u003cem\u003eMS\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the market share of firm \u003cem\u003ei\u003c/em\u003e, expressed as a percentage of the total.\u003c/p\u003e\u003cstrong\u003e4.2.2 Interest Rate Spread (IRS)\u003c/strong\u003e: Interest Rate Spread is a critical measure in banking that reflects the difference between the average rate a bank charges on its loans and the average rate it pays on its deposits. In this study, we use IRS as a proxy for Conduct within the Structure-Conduct-Performance (SCP) paradigm. The justification for choosing IRS is that it depicts a perfect picture of a bank\u0026rsquo;s pricing mechanism, risk management strategy and competitive behaviour in the market. By changing the interest rate on both loan and deposit side, a bank not only impacts its profit, but at the same time strategically signals to the market about the level of competitive engagement; kind of clients the bank is interested in attracting; and the share of the market it would like to control.\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eInterest Rate Spread (IRS) is calculated as the difference between the \u003cstrong\u003eweighted average lending rate\u003c/strong\u003e on all loans and the \u003cstrong\u003eaverage cost of deposits\u003c/strong\u003e. This measure gives a picture of the way in which the bank has behaved in relation to the rate of interest that they set for their loans and deposits.\u003c/p\u003e\n \u003c/div\u003eIRS\u0026thinsp;=\u0026thinsp;Weighted Average Interest Rate on Loans\u0026thinsp;\u0026minus;\u0026thinsp;Average Cost of Deposits\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eWeighted Average Interest Rate on Loans shows the interest rate applied to the bank\u0026rsquo;s loan portfolio by considering the proportions and interest rates of the loan segments (for example, retail, corporate, personal). The weight of each segment is calculated based on the proportion of total loans, and the weighted average interest rate is obtained using the following formula:\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:Weighted\\:Average\\:Interest\\:Rate\\:on\\:Loans=\\sum\\:_{i=1}^{n}(\\frac{{Loan\\:Segment}_{i}}{Total\\:Loans})\\:\\times\\:\\:{Interest\\:Rate}_{i}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:Average\\:cost\\:of\\:Deposits=\\:\\frac{Total\\:Interest\\:Paid\\:on\\:Deposits}{Total\\:Deposits}$$\u003c/div\u003e\n \u003c/div\u003e\u003cbr\u003e\n \u003cp\u003eTherefore, the IRS reflects the spread of what the bank earns from its loans and what it costs the bank to obtain deposits; in other words, it sums up the bank\u0026rsquo;s behaviour with respect to pricing policies and risk/reward balances in the banking sector. Higher IRS therefore points to more focus on profitability by charging higher prices for loans than the cost of deposits, while lower IRS signifies either offering lower prices in order to gain a wider market share (competitive pricing) or else holding lower-risk loans as a key strategy.\u003c/p\u003e\u003cstrong\u003e4.2.3 Performance\u003c/strong\u003e: We use Return on Assets (ROA) and Tobin\u0026rsquo;s Q as our performance indicators in different systems of equations. ROA, an accounting measure, is calculated by dividing a company\u0026apos;s earnings before interest and taxes (EBIT) by its total assets. Following Bertay, Demirg\u0026uuml;\u0026ccedil;-Kunt, \u0026amp; Huizinga (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Tobin\u0026rsquo;s Q, based on stock market prices, is computed using the formula:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:Tobi{n}^{{\\prime\\:}}s\\:Q=\\:\\frac{Market\\:Value\\:of\\:Equity+Book\\:Value\\:of\\:Preferred\\:equity\\:and\\:Liabilities}{Book\\:Value\\:of\\:Total\\:Assets}$$\u003c/div\u003e\n \u003c/div\u003e\u003cbr\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e is determined by dividing a bank\u0026apos;s earnings before interest and taxes (EBIT) by its total assets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Model\u003c/h2\u003e\n \u003cp\u003eTo investigate the interconnections between market structure, conduct, and performance, we propose a three-equation model. Each endogenous variable\u0026mdash;structure (CR4), conduct (IRS), and industry performance (measured by ROA)\u0026mdash;is modeled as a function of its own past values, as well as past values of the other variables and an exogenous factor.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCR4\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003eit\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e \u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e8\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIRS\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003eit\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e \u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e \u003csub\u003e\u003cem\u003e8\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026beta;\u003c/em\u003e \u003csub\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003eit\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e8\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u0026minus;3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is performance (measured by ROA); \u003cem\u003eIRS\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is Interest rate spread (conduct); \u003cem\u003eCR4\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is concentration (structure).\u003c/p\u003e\n \u003cp\u003eGiven the endogeneity of all but one variable in this study, we use a panel Vector Autoregression (PVAR) model. This method is suitable for analysing causal relationships among endogenous variables in dynamic systems (Mukhopadhyay \u0026amp; Chakraborty, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). PVAR accounts for dynamic interactions between variables through lagged terms within and across time series. To apply PVAR, we first verify the stationarity of the variables. Using panel unit root tests (Im et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Levin et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), we confirm that our panel data contains no unit roots (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnit root Test for variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eVariable\u003c/th\u003e\n \u003cth align=\"left\"\u003eMethod\u003c/th\u003e\n \u003cth align=\"left\"\u003eStatistic\u003c/th\u003e\n \u003cth align=\"left\"\u003eProb.**\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eROA\u003c/td\u003e\n \u003ctd align=\"left\"\u003eLevin, Lin \u0026amp; Chu t*\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-5.44846\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIm, Pesaran and Shin W-stat\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-3.26403\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.0006\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003eADF-Fisher Chi-square\u003c/td\u003e\n \u003ctd align=\"left\"\u003e75.2095\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.0035\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCR4\u003c/td\u003e\n \u003ctd align=\"left\"\u003eLevin, Lin \u0026amp; Chu t*\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-5.17817\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIm, Pesaran and Shin W-stat\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-1.7672\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.037\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003eADF-Fisher Chi-square\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.6555\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.0043\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIRS\u003c/td\u003e\n \u003ctd align=\"left\"\u003eLevin, Lin \u0026amp; Chu t*\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-5.35052\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIm, Pesaran and Shin W-stat\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-2.76055\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.0029\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003eADF-Fisher Chi-square\u003c/td\u003e\n \u003ctd align=\"left\"\u003e74.6546\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.0039\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003eAutomatic lag length selection based on Schwarz information criterion (SIC)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\u003cbr\u003e\n\u003c/div\u003e"},{"header":"Empirical Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Unit Root Tests\u003c/h2\u003e\n \u003cp\u003eThe results of the panel unit root tests (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) indicate that all variables (CR4, IRS, ROA, and Tobin\u0026apos;s Q) are stationary at levels. This satisfies the prerequisite for estimating the VAR model without the need for differencing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Lag Order Selection\u003c/h2\u003e\n \u003cp\u003eTables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e present the lag order selection criteria for the models with ROA and Tobin\u0026apos;s Q, respectively. For both models, the majority of the criteria (LR, FPE, HQ, and AIC) suggest an optimal lag length of 3. We therefore proceed with a lag order of 3 for both specifications.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLag order selection (CR4, IRS, and ROA as endogenous)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLag\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHQ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e896.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;7.756031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.35e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;7.783866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;7.801015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1694.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1568.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.51036\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.48e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.61771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.69030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1710.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.05214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.43245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.02e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.62031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.74732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1733.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.04512\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.42431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.02e-11\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.69268\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;14.87415\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eLR sequential modified LR test statistic (each test at 5% level), SC Schwarz information criterion, FPE final prediction error, HQ Hannan\u0026ndash;Quinn information criterion, AIC Akaike information criterion,\u003c/p\u003e\n \u003cp\u003e\u0026lowast; Lag order selected by the criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eP-VAR estimates (CR4, IRS, and ROA as endogenous)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIRS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR4 (\u0026minus;\u0026thinsp;1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.354702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.106302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05742)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06711)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((23.5875))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.60790))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;1.58457))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR4 (\u0026minus;\u0026thinsp;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.192044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.011752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05935)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;3.75531))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;1.36030))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.53898))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR4 (\u0026minus;\u0026thinsp;3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.176934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.003621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.02852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.03297)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;6.20532))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.75296))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.51998))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRS (\u0026minus;\u0026thinsp;1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.836901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.238692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.41355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.07102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.48413)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.36024))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((11.7845))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((2.55879))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRS (\u0026minus;\u0026thinsp;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.164775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.048927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.715841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.54720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.09318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.63891)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.30111))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.52521))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;1.12043))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRS (\u0026minus;\u0026thinsp;3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.138745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.36901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.42915)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.16333))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.50082))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.32340))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eROA (\u0026minus;\u0026thinsp;1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.062201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.000614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.848462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06922)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;1.04819))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.06091))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((12.2585))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eROA (\u0026minus;\u0026thinsp;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.009382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.074251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.07481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.08752)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.12541))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.18326))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.84845))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eROA (\u0026minus;\u0026thinsp;3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05625)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06587)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.93873))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.15661))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.58743))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.963512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdj. R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.929461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e() Contain Standard errors, (()) contain t-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnit root Test for Tobin\u0026rsquo;s Q\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb.**\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOBIN\u0026rsquo;S_Q\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevin, Lin \u0026amp; Chu t*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.89129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIm, Pesaran and Shin W-stat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADF-Fisher Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.3399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAutomatic lag length selection based on Schwarz information criterion (SIC)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Model with ROA\u003c/h2\u003e\n \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reveal several interesting relationships: Market Structure (CR4) exhibits strong persistence, with its first lag having a significant positive effect (1.354702) on current CR4. The second and third lags show significant negative effects, suggesting a cyclical pattern in market concentration. Conduct (IRS) also shows strong persistence, with its first lag having a significant positive effect (0.836901) on current IRS. Interestingly, the first lag of CR4 has a positive but insignificant effect on IRS, suggesting that increased market concentration may lead to higher interest rate spreads, albeit weakly. Performance (ROA) demonstrates high persistence, with its first lag having a significant positive effect (0.848462) on current ROA. The first lag of IRS has a significant positive effect (1.238692) on ROA, indicating that higher interest rate spreads lead to improved profitability in the short term.\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4 Robustness Check\u003c/h2\u003e\n \u003cp\u003eWhen we replace the ROA with Tobin\u0026rsquo;s Q as a measure of performance, the results remain similar. First we again check for the unit root (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) and choose the appropriate lag order (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The results in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e largely corroborate the findings from the ROA model, with some notable differences: \u003cstrong\u003eMarket Structure (CR4)\u003c/strong\u003e persistence remains strong, with coefficients similar to those in the ROA model. The persistence of IRS is slightly lower (0.804912) compared to the ROA model, but still significant. Tobin\u0026apos;s Q shows lower persistence (0.608291) compared to ROA. Interestingly, the first lag of CR4 has a positive but insignificant effect on Tobin\u0026apos;s Q, suggesting that market concentration may have a weak positive impact on market valuation.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLag order selection (CR4, IRS and TOBIN\u0026rsquo;S_Q as endogenous)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLag\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHQ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;3.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.81e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;3.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;3.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1137.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1430.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.647\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1152.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1183.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.412\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.52e-09\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;9.891\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;10.072\u0026lowast;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eLR\u003c/em\u003e sequential modified LR test statistic (each test at 5% level), \u003cem\u003eSC\u003c/em\u003e Schwarz information criterion, \u003cem\u003eFPE\u003c/em\u003e final prediction error, \u003cem\u003eHQ\u003c/em\u003e Hannan\u0026ndash;Quinn information criterion, \u003cem\u003eAIC\u003c/em\u003e Akaike information criterion,\u003c/p\u003e\n \u003cp\u003e\u0026lowast; Lag order selected by the criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eP-VAR estimates (CR4, IRS, and TOBIN\u0026rsquo;S_Q as endogenous)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIRS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTOBIN\u0026rsquo;S_Q\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR4 (\u0026minus;\u0026thinsp;1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.332105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.012543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.78942)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((22.1162))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.66136))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.28256))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR4 (\u0026minus;\u0026thinsp;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.184602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.004582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.628901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.68893)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;3.59972))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.53775))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.91225))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR4 (\u0026minus;\u0026thinsp;3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.176194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.004873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.532171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.02796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.37389)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;6.30102))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.96887))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.42302))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRS (\u0026minus;\u0026thinsp;1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.804912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.298021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.41381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06915)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.46521)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.32116))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((11.6432))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.97044))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRS (\u0026minus;\u0026thinsp;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.240219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.035218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.52982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.08921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.01135)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.45371))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.39474))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.02342))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRS (\u0026minus;\u0026thinsp;3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.758944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.35918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.78317)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.18084))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.72433))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.15861))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOBIN\u0026rsquo;S_Q (\u0026minus;\u0026thinsp;1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.000509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.608291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06827)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.05523))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.81918))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((9.09248))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOBIN\u0026rsquo;S_Q (\u0026minus;\u0026thinsp;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.001664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.334812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.07789)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.28503))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((0.20980))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((4.29817))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOBIN\u0026rsquo;S_Q (\u0026minus;\u0026thinsp;3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.000353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.182419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.06814)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;0.06734))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((1.83247))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e((\u0026minus;\u0026thinsp;2.67802))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.963123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.930642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdj. Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.961605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.737813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e() Contain Standard errors, (()) contain t-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n \u003cp\u003e---------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e----------------------\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study partially supports the SCP hypothesis, more specifically regarding the relationship between market concentration (CR4) and interest rate spreads (IRS). Profitability persistence in the Indian banking sector is consistent with the SCP framework, implying that banks in more concentrated markets enjoyed sustained profitability, supporting the arguments of Sinha and Sharma (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is consistent with the idea that banks can set prices to maximize short term profits as demonstrated by Banerjee and Savitha (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in the Indian life microinsurance market. The somewhat positive correlation between CR4 and IRS, however, implies that competition is still a moderating factor. According to O'Connell (2023) and Sahile et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), management effectiveness and macroeconomic conditions, as well as market concentration, may play important roles in explaining profitability. This is consistent with VanHoose's (2022) analysis of how regulatory actions and efficiency improvements may upend concentrated market structures and cast doubt on the concentration's long-term consequences on profitability.\u003c/p\u003e \u003cp\u003eAs in Mateev et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the cyclical pattern of market concentration in this study also accords with findings that competition affects the behaviour of banks, especially during crisis. He found that competition had pushed banks to raise their capital positions in the MENA region to manage risks, a pattern also reflected in our findings of persistent profitability in concentrated markets.\u003c/p\u003e \u003cp\u003eSimilar to Shair et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Saif-Alyousfi (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the role of competition in compressing profitability can be seen in the Indian banking sector. Market concentration may improve short run profitability but competitive pressures remain a factor in pricing strategy. This is consistent with Alhassan et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in who in Ghanaian context found that efficiency, not concentration, was the main determinant of profitability.\u003c/p\u003e \u003cp\u003eOur findings also indicate that while profitability (ROA) is enhanced in the short term by higher interest rate spreads, these gains are not fully reflected in market-based performance measures, such as Tobin\u0026rsquo;s Q. An alternative explanation is that market expectations of future competition or regulatory intervention are at play (Sinha and Sharma \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Overall, the results support neither the SCP nor the efficient structure hypothesis, but provide a partial support for both.\u003c/p\u003e \u003cp\u003eThe strong positive relation between lagged CR4 with current CR4, shows that market structure appears to persist over time, matching with the findings of Berger and Hannan (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) who found that market structure in banking is usually stable. The negative coefficients on the second and third lags of CR4, however, indicate a cyclical pattern that may be due to regulatory or competitive forces that periodically break up established market structures.\u003c/p\u003e \u003cp\u003eThe positive, albeit weak, relationship between lagged CR4 and IRS suggests that higher market concentration can lead to less competitive pricing, supporting the SCP hypothesis to some extent. This finding is in line with Bain\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1951\u003c/span\u003e) SCP theory, though the weak statistical significance mirrors Goldberg and Rai\u0026rsquo;s (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) results in European banking, where limited support for traditional SCP dynamics was observed. The significant positive effect of lagged IRS on ROA supports the efficient structure hypothesis (Demsetz, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), indicating that banks with market power can extract higher rents through higher spreads. However, the insignificant effect of IRS on Tobin's Q implies that the market does not value these short-term gains, likely anticipating future competitive or regulatory adjustments.\u003c/p\u003e \u003cp\u003eThe persistence of ROA compared to Tobin's Q suggests that accounting-based measures of performance are more stable than market-based measures in the Indian banking sector. The forward-looking nature of Tobin's Q, which incorporates market expectations of future performance and regulatory changes, may explain this discrepancy. The cyclical patterns observed in CR4 coefficients likely reflect the structural reforms in the Indian banking sector, including mergers of public sector banks and the entry of new private banks.\u003c/p\u003e"},{"header":"Conclusion, implications and future research","content":"\u003cp\u003eThis study provides partial support for the Structure-Conduct-Performance (SCP) hypothesis in the Indian banking sector, particularly in the relationship between market concentration and profitability. Our findings indicate that banks operating in more concentrated markets tend to enjoy sustained profitability in the short term, as reflected by the positive relationship between market concentration (CR4) and interest rate spreads (IRS). However, competition continues to moderate this relationship, and the effects of concentration are not fully captured by market-based performance measures like Tobin's Q. This suggests that market participants anticipate future competition or regulatory intervention, which could mitigate the benefits of concentration over time.\u003c/p\u003e \u003cp\u003eAdditionally, the cyclical nature of market concentration observed in the data points to the role of regulatory interventions and competitive forces in periodically disrupting established market structures. While the SCP hypothesis receives partial validation, the efficient structure hypothesis also plays a critical role in explaining bank profitability, particularly through the ability of more efficient banks to extract higher rents via interest rate spreads.\u003c/p\u003e \u003cp\u003eThe results have several implications for policymakers and banking institutions. First, the persistence of market concentration suggests that consolidation efforts, such as bank mergers, may reinforce market power and contribute to short-term profitability. However, regulators need to ensure that this concentration does not lead to excessive pricing power, which could harm consumer welfare. The weak relationship between concentration and pricing observed in this study underscores the importance of regulatory oversight to maintain competitive pressures in the sector.\u003c/p\u003e \u003cp\u003eFor banks, the findings highlight the importance of efficiency and strategic management. While concentration can lead to short-term profitability, sustained performance depends on the ability to manage competition and navigate regulatory changes. Banks should focus on improving operational efficiency to remain competitive in a dynamic market environment.\u003c/p\u003e \u003cp\u003eSeveral avenues for future research emerge from this study. First, further exploration of the cyclical nature of market concentration is warranted, particularly in light of ongoing regulatory reforms in the Indian banking sector. Future studies could examine the long-term effects of these structural changes, particularly in relation to market power, pricing strategies, and profitability.\u003c/p\u003e \u003cp\u003eSecond, the relationship between market concentration and risk-taking behaviour could be explored further. Studies such as Mateev et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have shown that competition can influence capital allocation and risk management strategies. Research focusing on how concentration impacts banks' risk profiles in India, especially under different regulatory regimes, could provide valuable insights.\u003c/p\u003e \u003cp\u003eFinally, the role of digitalization and technological disruption in the banking sector should be considered in future studies. As digital banking and fintech continue to reshape the competitive landscape, understanding how these innovations affect market structure, pricing, and profitability will be crucial for both researchers and practitioners.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure of Interest\u003c/strong\u003e: The authors report no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are publicly available in raw form. However, the specific variable calculations unique to this work cannot be shared until the Author 2 completes here doctoral work and may be made available on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research has no funding support\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDilawar Ahmad Bhat led the study\u0026apos;s conceptualization, data analysis, and manuscript drafting, managing the overall research direction and collaboration among authors. Shahida Rasheed conducted the literature review, assisted with data collection, and supported preliminary analyses. Himanshu Seth contributed expertise in statistical techniques, supporting the study\u0026apos;s methodology and result interpretation. Irshad Ahmad Malik provided feedback on drafts, ensuring contextual relevance and alignment with research aims. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to our respective institutions\u0026mdash;Symbiosis School of Banking and Finance, Pune; University of Kashmir, Srinagar; and IIM Rohtak, Haryana\u0026mdash;for their support throughout this research. We also appreciate the constructive feedback from our peers, which helped us enhance the rigour and clarity of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbrigo, M. R., \u0026amp; Love, I. (2016). Estimation of panel vector autoregression in Stata. \u003cem\u003eThe Stata Journal, 16\u003c/em\u003e(3), 778\u0026ndash;804. https://doi.org/10.1177/1536867X1601600314\u003c/li\u003e\n\u003cli\u003eAlhassan, A. L., Tetteh, M. 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The Banco Central do Brasil. https://www.bcb.gov.br/pec/wps/ingl/wps283.pdf\u003c/li\u003e\n\u003cli\u003eTirole, J. (1988). \u003cem\u003eThe theory of industrial organization\u003c/em\u003e. Cambridge: The MIT Press.\u003c/li\u003e\n\u003cli\u003eVanHoose, D. (2022). The Industrial Economics of Banking (3rd ed.). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-16241-1_4\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes ","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thehindubusinessline.com/money-and-banking/public-sector-banks-may-see-further-consolidation/article67647634.ece\u003c/span\u003e\u003cspan address=\"https://www.thehindubusinessline.com/money-and-banking/public-sector-banks-may-see-further-consolidation/article67647634.ece\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-economic-structures","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jecs","sideBox":"Learn more about [Journal of Economic Structures](http://journalofeconomicstructures.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jecs/default.aspx","title":"Journal of Economic Structures","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Banking consolidation, Market concentration, Structure-Conduct-Performance (SCP), Panel VAR analysis, Indian banking sector","lastPublishedDoi":"10.21203/rs.3.rs-5406984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5406984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates market structure, conduct, and performance in the context of the banking industry of India, and, in particular, the effects of consolidation. Using an unbalanced panel dataset comprising 30 banks from 2010 to 2020, the effects of changes in the degree of market concentration, interest rate spreads, and profitability are explored through panel Vector Auto-regression (PVAR) method. By employing Concentration Ratio (CR4) as a measure for consolidation, we analyse how the changes in structure of the sector affect the conduct and performance. Our findings indicate that banks operating in more concentrated markets tend to enjoy sustained profitability in the short term, as reflected by the positive relationship between market concentration (CR4) and interest rate spreads (IRS). However, competition continues to moderate this relationship, and the effects of concentration are not fully captured by market-based performance measures like Tobin's Q. This suggests that market participants anticipate future competition or regulatory intervention, which could mitigate the benefits of concentration over time. The findings provide important implications for the regulatory policy and managerial strategies of the banking system in view of the ongoing banking consolidation processes.\u003c/p\u003e\n\u003cp\u003eJEL Codes: G21, L11, C33, L13, E44\u003c/p\u003e","manuscriptTitle":"Testing SCP hypothesis amid growing consolidation in the Indian banking sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 17:42:32","doi":"10.21203/rs.3.rs-5406984/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2025-09-20T18:26:29+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-05-13T06:45:18+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-17T12:27:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-07T11:17:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Economic Structures","date":"2024-11-07T00:52:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-economic-structures","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jecs","sideBox":"Learn more about [Journal of Economic Structures](http://journalofeconomicstructures.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jecs/default.aspx","title":"Journal of Economic Structures","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b2c4906-fee0-4dbe-b84c-88f99e211a00","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:10:15+00:00","versionOfRecord":{"articleIdentity":"rs-5406984","link":"https://doi.org/10.1186/s40008-025-00361-6","journal":{"identity":"journal-of-economic-structures","isVorOnly":false,"title":"Journal of Economic Structures"},"publishedOn":"2025-10-31 15:57:46","publishedOnDateReadable":"October 31st, 2025"},"versionCreatedAt":"2024-12-17 17:42:32","video":"","vorDoi":"10.1186/s40008-025-00361-6","vorDoiUrl":"https://doi.org/10.1186/s40008-025-00361-6","workflowStages":[]},"version":"v1","identity":"rs-5406984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5406984","identity":"rs-5406984","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}